Machine learning, data engineering, and MLOps for engineers — Python for data analysis, ML fundamentals, RAG and vector databases, model deployment, and applied ML for predictive maintenance and forecasting.
Generate Python scripts for common engineering calculations, data processing, and report automation. Unit conversions, load calculations, and chart generation included.
Enter TP/FP/FN/TN counts and get accuracy, precision, recall, specificity, F1 score, and Matthews correlation coefficient.
Plan a train/validation/test split, see per-class sample counts with under-sized-split warnings, and get a recommended k-fold CV setting.
Data science and machine-learning engineering have no government license — competence is shown through vendor and platform certifications. This is an overview of the certifications that matter for ML/data engineers and data scientists, what each covers, who runs it, and how to prepare.
AWS ML – Specialty prep: data engineering, modeling, tuning, and deploying/operating models on AWS (SageMaker).
GCP Professional ML Engineer prep: problem framing, Vertex AI pipelines, productionizing and monitoring models.
TensorFlow Developer Certificate prep: a hands-on coding exam — CNNs, NLP, sequences and time series in TF/Keras.
Databricks ML Associate/Professional prep: Spark ML, MLflow, scalable feature engineering and the model lifecycle.
Hands-on ML from fundamentals through deep learning, NLP, RAG, and LoRA fine-tuning — with 34 real Python code blocks from scikit-learn to LangChain.
A four-part executive program: digital transformation frameworks, data strategy, a full board-ready AI capstone project, and a leadership playbook.